Table 1 Research using ML to detect eating disorder risk via survey responses

Buscema et al. [49]

172 females with a diagnosed ED

124 different variables: generic information, alimentary behaviour, eventual treatment and hospitalization, substance use, menstrual cycles, weight and height, hematochemical and instrumental examinations, psychodiagnostic tests

ED diagnosis

Feed forward neural networks

87%

Cross-sectional study

Small sample

Forrest et al. [27]

191 adults with BED in a randomised controlled trial

Treatment condition, demographic information, baseline clinical characteristics

(1) Binge eating abstinence; or (2) reduction; (3) ED psychopathology; (4) perceived weight loss; and (5) actual weight loss

Elastic net

(1) 51%; (2) 4%; (3) 27%; (4) 12%;

(5) 68%

Cross-sectional study

Small sample

Haynos et al. [26]

415 female adults with a diagnosed ED, of which 320 completed measures at Year 1, and 277 completed measures at Year 2

Demographics, psychiatric treatment, ED symptoms, other psychiatric diagnoses and symptoms, self-esteem

(1) ED diagnosis; (2) objective binge eating; (3) compensatory behaviours; and (4) underweight BMI

Elastic net regularized logistic regressions

At year 1:

(1) 62%; (2) 77%; (3) 88%; (4) 93%

At year 2:

(1) 61%; (2) 71%; (3) 85%; (4) 89%

Small sample

Krug et al. [33]

1402 adolescents and adults (92% female), with (n = 588) or without (n = 760) a diagnosed ED

Cross-cultural risk factors for EDs before the age of 12

(1) ED onset; (2) differential ED diagnoses

Penalised logistic regression (LASSO)

(1) 89%; (2) 70%

Cross-sectional study

Linardon et al. [30]

1341 adults (91% females), with (n = 512) and without (n = 829) recurrent binge eating

Intuitive eating behaviours, flexible restraint behaviours, rigid restraint behaviours, rigid restraint cognitions

Recurrent binge eating behaviour

Decision tree classification

70%

Cross-sectional study

Orru et al. [31]

107 females, with (n = 53) and without (n = 54) a diagnosed ED

Presence of manic/hypomanic and depressive symptoms, AN and BN symptoms

ED status

Naïve bayes

91%

Cross-sectional study

Small sample

Ren et al. [34]

830 non-clinical young females

Psychological distress, eating inflexibility, body image inflexibility, body dissatisfaction, emotional overeating, loss of control overeating, body mass index

ED risk

Decision tree classification

85%

Cross-sectional study

Rosenfield and Linstead [32]

44 female young adults, with (n = 20) and without (n = 24) a previous diagnosis of AN

ED symptoms, psychosocial impairment, symptoms of autism spectrum disorder

ED status

K-means clustering

78%

Cross-sectional study

Small sample